low xarray.corr() performance on big datasets

xarray-v0.16.2-correlation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray, dask, numpy\n",
    "import dask.distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open Dask cluster,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/miniconda3/lib/python3.8/site-packages/distributed/node.py:151: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 43455 instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "cluster = dask.distributed.LocalCluster(\n",
    "    n_workers=1,\n",
    "    threads_per_worker=4,\n",
    "    memory_limit='8GB',\n",
    "    ip='0.0.0.0'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = dask.distributed.Client(cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://10.199.124.111:39555</li>\n",
       "  <li><b>Dashboard: </b><a href='http://10.199.124.111:43455/status' target='_blank'>http://10.199.124.111:43455/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>1</li>\n",
       "  <li><b>Cores: </b>4</li>\n",
       "  <li><b>Memory: </b>8.00 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://10.199.124.111:39555' processes=1 threads=4, memory=8.00 GB>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a correlation coefficient suitable for big data application,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_big_data_corr(a, b, dim):\n",
    "    a, b = xarray.align(a, b)\n",
    "    valid_values = a.notnull() & b.notnull()\n",
    "    a = a.where(valid_values)\n",
    "    b = b.where(valid_values)\n",
    "    c = (a * b).mean(dim) - ( a.mean(dim) * b.mean(dim) )\n",
    "    c /= a.std(dim)\n",
    "    c /= b.std(dim)\n",
    "    return c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check equivalence with xarray.corr() for unaligned data with nan-values,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = 250; time = numpy.linspace(0, 2*numpy.pi, length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_a = xarray.DataArray(\n",
    "    numpy.sin(time) + numpy.random.normal(0, 0.25, length),\n",
    "    coords=[time], dims=\"time\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_b = xarray.DataArray(\n",
    "    numpy.sin(time) + numpy.random.normal(0, 0.25, length),\n",
    "    coords=[time], dims=\"time\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_choice(fraction):\n",
    "    return numpy.sort(numpy.random.choice(length, size=int(length*fraction), replace=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure data arrays are not aligned\n",
    "da_a = da_a.isel(time=random_choice(0.8)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure nan-values are present\n",
    "#mask = ~numpy.isin(numpy.arange(length), random_choice(0.2))\n",
    "#da_b = da_b.where(mask) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure there are nan-values present\n",
    "da_b = da_b.where(da_b > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14.5 ms, sys: 2.26 ms, total: 16.7 ms\n",
      "Wall time: 15.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "corr_orig = xarray.corr(da_a, da_b, \"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 56 µs, sys: 26 µs, total: 82 µs\n",
      "Wall time: 83.9 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(0.61706519)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "corr_orig.compute().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.16 ms, sys: 627 µs, total: 3.79 ms\n",
      "Wall time: 3.67 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "corr_big_data = calc_big_data_corr(da_a, da_b, \"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 55 µs, sys: 25 µs, total: 80 µs\n",
      "Wall time: 82.3 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(0.61706519)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "corr_big_data.compute().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now with bigger data,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_a = xarray.DataArray(\n",
    "  dask.array.random.normal(size=(100_000, ), chunks=(200, ))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_b = xarray.DataArray(\n",
    "  dask.array.random.normal(size=(100_000, ), chunks=(200, ))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure there are nan-values present\n",
    "da_b = da_b.where(da_b > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.23 s, sys: 248 ms, total: 5.47 s\n",
      "Wall time: 9.88 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "corr_orig = xarray.corr(da_a, da_b, dim=\"dim_0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.92 s, sys: 197 ms, total: 4.12 s\n",
      "Wall time: 8.5 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(-0.00190346)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "corr_orig.compute().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 32.6 ms, sys: 8 µs, total: 32.6 ms\n",
      "Wall time: 32 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "corr_big_data = calc_big_data_corr(da_a, da_b, dim=\"dim_0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.66 s, sys: 166 ms, total: 3.83 s\n",
      "Wall time: 7.91 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(-0.00190346)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "corr_big_data.compute().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python environment,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# packages in environment at /miniconda3:\n",
      "#\n",
      "# Name                    Version                   Build  Channel\n",
      "_libgcc_mutex             0.1                 conda_forge    conda-forge\n",
      "_openmp_mutex             4.5                       1_gnu    conda-forge\n",
      "aiohttp                   3.7.3            py38h497a2fe_1    conda-forge\n",
      "argon2-cffi               20.1.0           py38h497a2fe_2    conda-forge\n",
      "asciitree                 0.3.3                      py_2    conda-forge\n",
      "async-timeout             3.0.1                   py_1000    conda-forge\n",
      "async_generator           1.10                       py_0    conda-forge\n",
      "attrs                     20.3.0             pyhd3deb0d_0    conda-forge\n",
      "backcall                  0.2.0              pyh9f0ad1d_0    conda-forge\n",
      "backports                 1.0                        py_2    conda-forge\n",
      "backports.functools_lru_cache 1.6.1                      py_0    conda-forge\n",
      "bleach                    3.2.1              pyh9f0ad1d_0    conda-forge\n",
      "bokeh                     2.2.3            py38h578d9bd_0    conda-forge\n",
      "brotlipy                  0.7.0           py38h27cfd23_1003  \n",
      "bzip2                     1.0.8                h7f98852_4    conda-forge\n",
      "c-ares                    1.17.1               h36c2ea0_0    conda-forge\n",
      "ca-certificates           2020.12.8            h06a4308_0  \n",
      "cairo                     1.16.0            h7979940_1007    conda-forge\n",
      "cartopy                   0.18.0           py38h9b98833_8    conda-forge\n",
      "cartopy_offlinedata       0.2.3              pyh9f0ad1d_0    conda-forge\n",
      "certifi                   2020.12.5        py38h578d9bd_1    conda-forge\n",
      "cffi                      1.14.3           py38h261ae71_2  \n",
      "cftime                    1.3.0            py38h0b5ebd8_0    conda-forge\n",
      "chardet                   3.0.4           py38h06a4308_1003  \n",
      "click                     7.1.2              pyh9f0ad1d_0    conda-forge\n",
      "cloudpickle               1.6.0                      py_0    conda-forge\n",
      "colorcet                  2.0.2                      py_0  \n",
      "conda                     4.9.2            py38h578d9bd_0    conda-forge\n",
      "conda-package-handling    1.7.2            py38h03888b9_0  \n",
      "cryptography              3.2.1            py38h3c74f83_1  \n",
      "curl                      7.71.1               he644dc0_8    conda-forge\n",
      "cycler                    0.10.0                     py_2    conda-forge\n",
      "cytoolz                   0.11.0           py38h25fe258_1    conda-forge\n",
      "dask                      2020.12.0          pyhd8ed1ab_0    conda-forge\n",
      "dask-core                 2020.12.0          pyhd8ed1ab_0    conda-forge\n",
      "dask-jobqueue             0.7.2              pyhd8ed1ab_0    conda-forge\n",
      "dask-labextension         4.0.1              pyhd8ed1ab_0    conda-forge\n",
      "dbus                      1.13.18              hb2f20db_0  \n",
      "decorator                 4.4.2                      py_0    conda-forge\n",
      "defusedxml                0.6.0                      py_0    conda-forge\n",
      "distributed               2020.12.0        py38h578d9bd_0    conda-forge\n",
      "entrypoints               0.3             py38h32f6830_1002    conda-forge\n",
      "expat                     2.2.10               he6710b0_2  \n",
      "fasteners                 0.16               pyhd3eb1b0_0  \n",
      "fontconfig                2.13.1            h736d332_1003    conda-forge\n",
      "freetype                  2.10.4               he06d7ca_0    conda-forge\n",
      "fribidi                   1.0.10               h516909a_0    conda-forge\n",
      "fsspec                    0.8.5              pyhd8ed1ab_0    conda-forge\n",
      "geos                      3.8.1                he1b5a44_0    conda-forge\n",
      "geoviews                  1.5.0                      py_0    conda-forge\n",
      "gettext                   0.19.8.1          h0b5b191_1005    conda-forge\n",
      "git                       2.30.0          pl5320h014a29a_0    conda-forge\n",
      "glib                      2.66.4               h709712a_1    conda-forge\n",
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      "hdf5                      1.10.6          nompi_h6a2412b_1114    conda-forge\n",
      "heapdict                  1.0.1                      py_0    conda-forge\n",
      "holoviews                 1.14.1             pyhd3deb0d_0    conda-forge\n",
      "hvplot                    0.7.0              pyhd3deb0d_0    conda-forge\n",
      "icu                       68.1                 h58526e2_0    conda-forge\n",
      "idna                      2.10                       py_0  \n",
      "importlib-metadata        3.4.0            py38h578d9bd_0    conda-forge\n",
      "importlib_metadata        3.4.0                hd8ed1ab_0    conda-forge\n",
      "ipykernel                 5.4.2            py38h81c977d_0    conda-forge\n",
      "ipython                   7.19.0           py38h81c977d_1    conda-forge\n",
      "ipython_genutils          0.2.0                      py_1    conda-forge\n",
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      "libedit                   3.1.20191231         h14c3975_1  \n",
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      "libevent                  2.1.10               hcdb4288_3    conda-forge\n",
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      "locket                    0.2.0                      py_2    conda-forge\n",
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      "lzo                       2.10              h516909a_1000    conda-forge\n",
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      "matplotlib                3.3.3            py38h578d9bd_0    conda-forge\n",
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      "mistune                   0.8.4           py38h497a2fe_1003    conda-forge\n",
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      "msgpack-python            1.0.2            py38h1fd1430_1    conda-forge\n",
      "multidict                 5.1.0            py38h497a2fe_1    conda-forge\n",
      "mysql-common              8.0.22               ha770c72_1    conda-forge\n",
      "mysql-libs                8.0.22               h1fd7589_1    conda-forge\n",
      "nbclient                  0.5.1                      py_0    conda-forge\n",
      "nbconvert                 6.0.7            py38h578d9bd_3    conda-forge\n",
      "nbformat                  5.0.8                      py_0    conda-forge\n",
      "ncurses                   6.2                  he6710b0_1  \n",
      "nest-asyncio              1.4.3              pyhd8ed1ab_0    conda-forge\n",
      "netcdf4                   1.5.5.1         nompi_py38h1cdf482_100    conda-forge\n",
      "nodejs                    15.3.0               h25f6087_0    conda-forge\n",
      "notebook                  6.1.6            py38h578d9bd_0    conda-forge\n",
      "nspr                      4.29                 he1b5a44_1    conda-forge\n",
      "nss                       3.60                 hb5efdd6_0    conda-forge\n",
      "numcodecs                 0.7.2            py38h709712a_1    conda-forge\n",
      "numpy                     1.19.5           py38h18fd61f_1    conda-forge\n",
      "olefile                   0.46               pyh9f0ad1d_1    conda-forge\n",
      "openssl                   1.1.1i               h7f98852_0    conda-forge\n",
      "packaging                 20.8               pyhd3deb0d_0    conda-forge\n",
      "pandas                    1.2.0            py38h51da96c_0    conda-forge\n",
      "pandoc                    2.11.3.2             h7f98852_0    conda-forge\n",
      "pandocfilters             1.4.3            py38h06a4308_1  \n",
      "panel                     0.10.2             pyhd8ed1ab_0    conda-forge\n",
      "pango                     1.42.4               h80147aa_5    conda-forge\n",
      "param                     1.10.1             pyhd3deb0d_0    conda-forge\n",
      "parso                     0.7.1              pyh9f0ad1d_0    conda-forge\n",
      "partd                     1.1.0                      py_0    conda-forge\n",
      "pcre                      8.44                 he1b5a44_0    conda-forge\n",
      "perl                      5.32.0               h36c2ea0_0    conda-forge\n",
      "pexpect                   4.8.0            py38h32f6830_1    conda-forge\n",
      "pickleshare               0.7.5           py38h32f6830_1002    conda-forge\n",
      "pillow                    8.1.0            py38h357d4e7_1    conda-forge\n",
      "pip                       20.2.4           py38h06a4308_0  \n",
      "pixman                    0.40.0               h36c2ea0_0    conda-forge\n",
      "proj                      7.2.0                he47e99f_1    conda-forge\n",
      "prometheus_client         0.9.0              pyhd3deb0d_0    conda-forge\n",
      "prompt-toolkit            3.0.10             pyha770c72_0    conda-forge\n",
      "prompt_toolkit            3.0.10               hd8ed1ab_0    conda-forge\n",
      "psutil                    5.8.0            py38h497a2fe_1    conda-forge\n",
      "ptyprocess                0.7.0              pyhd3deb0d_0    conda-forge\n",
      "pycosat                   0.6.3            py38h7b6447c_1  \n",
      "pycparser                 2.20                       py_2  \n",
      "pyct                      0.4.8                    py38_0  \n",
      "pygments                  2.7.3              pyhd8ed1ab_0    conda-forge\n",
      "pyopenssl                 19.1.0             pyhd3eb1b0_1  \n",
      "pyparsing                 2.4.7              pyh9f0ad1d_0    conda-forge\n",
      "pyqt                      5.12.3           py38h578d9bd_7    conda-forge\n",
      "pyqt-impl                 5.12.3           py38h7400c14_7    conda-forge\n",
      "pyqt5-sip                 4.19.18          py38h709712a_7    conda-forge\n",
      "pyqtchart                 5.12             py38h7400c14_7    conda-forge\n",
      "pyqtwebengine             5.12.1           py38h7400c14_7    conda-forge\n",
      "pyrsistent                0.17.3           py38h497a2fe_2    conda-forge\n",
      "pyshp                     2.1.2              pyh9f0ad1d_0    conda-forge\n",
      "pysocks                   1.7.1            py38h06a4308_0  \n",
      "python                    3.8.5                h7579374_1  \n",
      "python-dateutil           2.8.1                      py_0    conda-forge\n",
      "python-graphviz           0.16               pyhd3deb0d_1    conda-forge\n",
      "python_abi                3.8                      1_cp38    conda-forge\n",
      "pytz                      2020.5             pyhd8ed1ab_0    conda-forge\n",
      "pyviz_comms               2.0.1              pyhd3deb0d_0    conda-forge\n",
      "pyyaml                    5.3.1            py38h497a2fe_2    conda-forge\n",
      "pyzmq                     20.0.0           py38h3d7ac18_1    conda-forge\n",
      "qt                        5.12.9               h9d6b050_2    conda-forge\n",
      "qtconsole                 5.0.1              pyhd8ed1ab_0    conda-forge\n",
      "qtpy                      1.9.0                      py_0    conda-forge\n",
      "readline                  8.0                  h7b6447c_0  \n",
      "rechunker                 0.3.2                    pypi_0    pypi\n",
      "reproc                    14.2.1               h36c2ea0_0    conda-forge\n",
      "reproc-cpp                14.2.1               h58526e2_0    conda-forge\n",
      "requests                  2.24.0                     py_0  \n",
      "ruamel_yaml               0.15.87          py38h7b6447c_1  \n",
      "scikit-learn              0.24.0           py38h658cfdd_0    conda-forge\n",
      "scipy                     1.6.0            py38hb2138dd_0    conda-forge\n",
      "send2trash                1.5.0                      py_0    conda-forge\n",
      "setuptools                50.3.1           py38h06a4308_1  \n",
      "shapely                   1.7.1            py38hc7361b7_1    conda-forge\n",
      "simpervisor               0.4                pyhd8ed1ab_0    conda-forge\n",
      "six                       1.15.0           py38h06a4308_0  \n",
      "sortedcontainers          2.3.0              pyhd8ed1ab_0    conda-forge\n",
      "sqlite                    3.34.0               h74cdb3f_0    conda-forge\n",
      "tblib                     1.7.0                      py_0  \n",
      "terminado                 0.9.2            py38h578d9bd_0    conda-forge\n",
      "testpath                  0.4.4                      py_0    conda-forge\n",
      "threadpoolctl             2.1.0              pyh5ca1d4c_0    conda-forge\n",
      "tk                        8.6.10               hbc83047_0  \n",
      "toolz                     0.11.1                     py_0    conda-forge\n",
      "tornado                   6.1              py38h497a2fe_1    conda-forge\n",
      "tqdm                      4.51.0             pyhd3eb1b0_0  \n",
      "traitlets                 5.0.5                      py_0    conda-forge\n",
      "typing-extensions         3.7.4.3                       0    conda-forge\n",
      "typing_extensions         3.7.4.3                    py_0    conda-forge\n",
      "urllib3                   1.25.11                    py_0  \n",
      "wcwidth                   0.2.5              pyh9f0ad1d_2    conda-forge\n",
      "webencodings              0.5.1                      py_1    conda-forge\n",
      "wheel                     0.35.1             pyhd3eb1b0_0  \n",
      "widgetsnbextension        3.5.1            py38h578d9bd_4    conda-forge\n",
      "xarray                    0.16.2             pyhd8ed1ab_0    conda-forge\n",
      "xoak                      0.1.0              pyhd8ed1ab_0    conda-forge\n",
      "xorg-kbproto              1.0.7             h14c3975_1002    conda-forge\n",
      "xorg-libice               1.0.10               h516909a_0    conda-forge\n",
      "xorg-libsm                1.2.3             h84519dc_1000    conda-forge\n",
      "xorg-libx11               1.6.12               h516909a_0    conda-forge\n",
      "xorg-libxext              1.3.4                h516909a_0    conda-forge\n",
      "xorg-libxpm               3.5.13               h516909a_0    conda-forge\n",
      "xorg-libxrender           0.9.10            h516909a_1002    conda-forge\n",
      "xorg-libxt                1.1.5             h516909a_1003    conda-forge\n",
      "xorg-renderproto          0.11.1            h14c3975_1002    conda-forge\n",
      "xorg-xextproto            7.3.0             h14c3975_1002    conda-forge\n",
      "xorg-xproto               7.0.31            h14c3975_1007    conda-forge\n",
      "xz                        5.2.5                h7b6447c_0  \n",
      "yaml                      0.2.5                h7b6447c_0  \n",
      "yarl                      1.6.3            py38h497a2fe_1    conda-forge\n",
      "zarr                      2.6.1              pyhd8ed1ab_0    conda-forge\n",
      "zeromq                    4.3.3                h58526e2_3    conda-forge\n",
      "zict                      2.0.0                      py_0    conda-forge\n",
      "zipp                      3.4.0                      py_0    conda-forge\n",
      "zlib                      1.2.11               h7b6447c_3  \n",
      "zstd                      1.4.8                ha95c52a_1    conda-forge\n"
     ]
    }
   ],
   "source": [
    "!conda list"
   ]
  }
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